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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
Registro Completo |
Biblioteca(s): |
Embrapa Gado de Corte. |
Data corrente: |
23/03/2017 |
Data da última atualização: |
23/03/2017 |
Tipo da produção científica: |
Artigo de Divulgação na Mídia |
Autoria: |
SIQUEIRA, F. |
Afiliação: |
FABIANE SIQUEIRA, CNPGC. |
Título: |
Genômica e melhoramento genético em bovinos. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
AGRON, ano, 4, n. 37, p. 24-27. abr. 2016. |
Idioma: |
Português |
Palavras-Chave: |
Genomica. |
Thesagro: |
Bovino; DNA; Genética molecular; Genoma; Marcador molecular; Melhoramento genético animal. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00510nam a2200181 a 4500 001 2067572 005 2017-03-23 008 2016 bl uuuu u00u1 u #d 100 1 $aSIQUEIRA, F. 245 $aGenômica e melhoramento genético em bovinos.$h[electronic resource] 260 $aAGRON, ano, 4, n. 37, p. 24-27. abr. 2016.$c2016 650 $aBovino 650 $aDNA 650 $aGenética molecular 650 $aGenoma 650 $aMarcador molecular 650 $aMelhoramento genético animal 653 $aGenomica
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Registro Completo
Biblioteca(s): |
Embrapa Uva e Vinho. |
Data corrente: |
15/10/2021 |
Data da última atualização: |
15/10/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
LIMA, M. dos S.; PEREIRA, G. E.; FEDRIGO, I. M. T. |
Afiliação: |
MARCOS DOS SANTOS LIMA, Department of Food Technology, Federal Institute of Sertão Pernambucano, Campus Petrolina, BR 407 km 08 RdJardim São Paulo, Petrolina, PE 56314-522, Brazil; GIULIANO ELIAS PEREIRA, CNPUV; ISABELA MAIA TOALDO FEDRIGO, Department of Food Science and Technology, Federal University of Santa Catarina, Admar Gonzaga Rd., 1346, Itacorubi, Florianópolis, SC 88034-001, Brazil. |
Título: |
Artifcial neural network: a powerful tool in associating phenolic compounds with antioxidant activity of grape juices. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Food Analytical Methods, 14 oct. 2021. Online. |
DOI: |
10.1007/s12161-021-02144-8 |
Idioma: |
Inglês |
Conteúdo: |
In vitro techniques are essential to assess the antioxidant potential of foods, although methods with diferent action mechanisms make troublesome data analysis. This article describes the use of artifcial neural network (ANN) to associate phenolic compounds with antioxidant activity in vitro (AOX) of grape juices. A multilayer perceptron (MLP) ANN was obtained with 28 phenolics quantifed, as input layers, and AOX measuring by DPPH, ABTS, FRAP, H2O2, and β-carotene/linoleic acid bleaching assay (βCLA) methods, as output layers. To improve discussion in food sciences, the ANN results were compared with Pearson?s correlation and principal component analysis (PCA), methods largely used in food studies. Pearson?s technique showed correlations between antioxidant methods and some of the phenolic compounds, but with limitations. PCA proved to be a more powerful method than Pearson?s correlation, as it positively associated 13 phenolics with four out of fve antioxidant methods. The MLP-ANN allowed simultaneous association of 19 individual phenolics, while a single hidden layer predicted 15 phenolics with simultaneous action in all AOX methods. The power of association was: ANN>PCA>Pearson. It was evidenced that ANN is a powerful tool for screening antioxidants in diferent AOX systems, which is applicable in health interests. |
Palavras-Chave: |
Antioxidant methods; Bioactivity; Grape polyphenol. |
Thesaurus NAL: |
Chemometrics. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/226970/1/SantosLima2021-Article-ArtificialNeuralNetworkAPowerf.pdf
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Marc: |
LEADER 02004naa a2200205 a 4500 001 2135356 005 2021-10-15 008 2021 bl uuuu u00u1 u #d 024 7 $a10.1007/s12161-021-02144-8$2DOI 100 1 $aLIMA, M. dos S. 245 $aArtifcial neural network$ba powerful tool in associating phenolic compounds with antioxidant activity of grape juices.$h[electronic resource] 260 $c2021 520 $aIn vitro techniques are essential to assess the antioxidant potential of foods, although methods with diferent action mechanisms make troublesome data analysis. This article describes the use of artifcial neural network (ANN) to associate phenolic compounds with antioxidant activity in vitro (AOX) of grape juices. A multilayer perceptron (MLP) ANN was obtained with 28 phenolics quantifed, as input layers, and AOX measuring by DPPH, ABTS, FRAP, H2O2, and β-carotene/linoleic acid bleaching assay (βCLA) methods, as output layers. To improve discussion in food sciences, the ANN results were compared with Pearson?s correlation and principal component analysis (PCA), methods largely used in food studies. Pearson?s technique showed correlations between antioxidant methods and some of the phenolic compounds, but with limitations. PCA proved to be a more powerful method than Pearson?s correlation, as it positively associated 13 phenolics with four out of fve antioxidant methods. The MLP-ANN allowed simultaneous association of 19 individual phenolics, while a single hidden layer predicted 15 phenolics with simultaneous action in all AOX methods. The power of association was: ANN>PCA>Pearson. It was evidenced that ANN is a powerful tool for screening antioxidants in diferent AOX systems, which is applicable in health interests. 650 $aChemometrics 653 $aAntioxidant methods 653 $aBioactivity 653 $aGrape polyphenol 700 1 $aPEREIRA, G. E. 700 1 $aFEDRIGO, I. M. T. 773 $tFood Analytical Methods, 14 oct. 2021. Online.
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